Papudeshi Bhavya, Roach Michael J, Mallawaarachchi Vijini, Bouras George, Grigson Susanna R, Giles Sarah K, Harker Clarice M, Hutton Abbey L K, Tarasenko Anita, Inglis Laura K, Vega Alejandro A, Souza Cole, Boling Lance, Hajama Hamza, Cobián Güemes Ana Georgina, Segall Anca M, Dinsdale Elizabeth A, Edwards Robert A
Flinders Accelerator for Microbiome Exploration, College of Science of Engineering, Flinders University, Adelaide, South Australia 5042, Australia.
Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, South Australia 5042, Australia.
Bioinform Adv. 2025 Jan 17;5(1):vbaf004. doi: 10.1093/bioadv/vbaf004. eCollection 2025.
Phage therapy offers a viable alternative for bacterial infections amid rising antimicrobial resistance. Its success relies on selecting safe and effective phage candidates that require comprehensive genomic screening to identify potential risks. However, this process is often labor intensive and time-consuming, hindering rapid clinical deployment.
We developed Sphae, an automated bioinformatics pipeline designed to streamline the therapeutic potential of a phage in under 10 minutes. Using Snakemake workflow manager, Sphae integrates tools for quality control, assembly, genome assessment, and annotation tailored specifically for phage biology. Sphae automates the detection of key genomic markers, including virulence factors, antimicrobial resistance genes, and lysogeny indicators such as integrase, recombinase, and transposase, which could preclude therapeutic use. Among the 65 phage sequences analyzed, 28 showed therapeutic potential, 8 failed due to low sequencing depth, 22 contained prophage or virulent markers, and 23 had multiple phage genomes. This workflow produces a report to assess phage safety and therapy suitability quickly. Sphae is scalable and portable, facilitating efficient deployment across most high-performance computing and cloud platforms, accelerating the genomic evaluation process.
Sphae source code is freely available at https://github.com/linsalrob/sphae, with installation supported on Conda, PyPi, Docker containers.
在抗菌药物耐药性不断上升的情况下，噬菌体疗法为细菌感染提供了一种可行的替代方案。其成功依赖于选择安全有效的噬菌体候选物，这需要进行全面的基因组筛选以识别潜在风险。然而，这个过程通常 labor intensive 且耗时，阻碍了快速的临床应用。
我们开发了Sphae，这是一个自动化的生物信息学流程，旨在在不到10分钟的时间内简化噬菌体的治疗潜力评估。利用Snakemake工作流管理器，Sphae整合了专门针对噬菌体生物学的质量控制、组装、基因组评估和注释工具。Sphae能自动检测关键的基因组标记，包括毒力因子、抗菌药物耐药基因以及整合酶、重组酶和转座酶等溶原性指标，这些指标可能会妨碍治疗用途。在分析的65个噬菌体序列中，28个显示出治疗潜力，8个因测序深度低而失败，22个含有前噬菌体或毒性标记，23个有多个噬菌体基因组。这个工作流程会生成一份报告，以便快速评估噬菌体的安全性和治疗适用性。Sphae具有可扩展性和便携性，便于在大多数高性能计算和云平台上高效部署，加速基因组评估过程。
Sphae的源代码可在https://github.com/linsalrob/sphae上免费获取，在Conda、PyPi、Docker容器上均支持安装。
